Using GRACE Satellite Observations for Separating Meteorological

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OPEN Using GRACE satellite observations for separating meteorological variability from anthropogenic impacts on water availability Seyed‑Mohammad Hosseini‑Moghari 1, Shahab Araghinejad2, Kumars Ebrahimi3, Qiuhong Tang 1,4 & Amir AghaKouchak5,6*

Gravity Recovery and Climate Experiment (GRACE) observations provide information on Total Water Storage Anomaly (TWSA) which is a key variable for drought monitoring and assessment. The so-called Total Water Storage Defcit Index (TWSDI) based on GRACE data has been widely used for characterizing drought events. Here we show that the commonly used TWSDI approach often exhibits signifcant inconsistencies with meteorological conditions, primarily upon presence of a trend in observations due to anthropogenic water use. In this study, we propose a modifed version of TWSDI (termed, MTWSDI) that decomposes the anthropogenic and climatic-driven components of GRACE observations. We applied our approach for drought monitoring over the Ganges–Brahmaputra in India and Markazi basins in Iran. Results show that the newly developed MTWSDI exhibits consistency with meteorological drought indices in both basins. We also propose a defcit-based method for drought monitoring and recovery assessment using GRACE observations, providing useful information about volume of defcit, and minimum and average time for drought recovery. According to the defcit thresholds, water defcits caused by anthropogenic impacts every year in the Ganges–Brahmaputra basin and Markazi basins is almost equal to an abnormally dry condition and a moderate drought condition, receptively. It indicates that unsustainable human water use have led to a form of perpetual and accelerated anthropogenic drought in these basins. Continuation of this trend would deplete the basin and cause signifcant socio-economic challenges.

Drought is a multifaceted natural phenomenon that occurs across a wide range of spatio-temporal scales 1 with adverse environmental, social and economic impacts 2, 3. Drought ofen leads to reduction in agricultural production4, 5, job losses6, 7, more frequent dust storms8, 9, more intense forest fres9, 10, and many other negative impacts11–13. For instance, in the frst half of 2001, drought caused $2.6 billion economic loss in Iran 14. In 2014, California drought caused $2.2 billion economic loss and 17,100 job losses6. Also, 25–45% reduction in crop yield across Australia during the millennium drought periods has been reported 4. On the other hand, global warming and climate change is expected to increase the frequency and intensity of droughts 15–18. In today’s world with limited water resources and ever increasing water demands, impacts of drought can become more intense as a result of population growth and more competition for water; a phenomenon termed anthropogenic drought 19. Terefore, developing drought monitoring and predictions are fundamental to reduce drought impacts. In recent decades, drought monitoring has improved substantially using satellite vegetation health products20–22, ground-based and satellite precipitation observations 23–25, soil moisture data26–28, and numerical model simulations 28–30. However, performance of these products and numerical models needs to be verifed for operational application. Diferent datasets and monitoring systems have their own advantages and limitations and

1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. 2Stantec Consulting Company, Sacramento, CA 95816, USA. 3Department of Irrigation and Reclamation Engineering, University of Tehran, 14378‑35693 Karaj, Iran. 4University of Chinese Academy of Sciences, Beijing 100049, China. 5Department of Civil and Environmental Engineering, University of California, Irvine 92697, USA. 6Department of Earth System Science, University of California, Irvine 92697, USA. *email: [email protected]

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Figure 1. Location of the studied basins (lef) and the root mean square (RMS) of TWSA over the basins (right).

they do not capture all aspects of droughts, particularly groundwater and total water storage 31. Afer launching Gravity Recovery and Climate Experiment (GRACE) satellite in March 2002, a unique dataset on Total Water Storage Anomaly (TWSA) has become available worldwide 32–34. GRACE data has been evaluated in a variety of studies and has also been used as a benchmark and/or input for evaluating or improving hydrological/land surface model simulations32, 35–39. Owing to uniqueness and global coverage of GRACE observations, it has been widely used in drought monitoring studies 38, 40–49. Although GRACE provides valuable data for drought monitoring worldwide, its application in heavy irrigated/managed regions, that are ofen more vulnerable to droughts, is not straightforward. GRACE observations include not only climatic information, but also human factors 50–53. In regions with intensive human activities, for example, GRACE signals may be afected by human induced mass changes54, 55. In areas where water consumption is higher than replenishment rate, GRACE observations typically indicate a decreasing trend. While this information is valuable and provides insights on anthropogenic water use, it poses a challenge specially where we observed a signifcant downward trend: When standardizing TWSA time series, at the start, middle and end years of time series, we typically observe wet, near normal and dry years, respectively, primarily dominated by a downward trend 56. As a result, as new observations are recorded, a past drought event may be classifed into a normal or even wet period afer re-standardization of TWSA data. For instance, monitoring drought over the Tigris-Euphrates basin using GRACE observations, Chao et al.57 stated that the Tigris-Euphrates basin under- went a severe drought from September 2007 to end of 2015. Tis conclusion appears to be inconsistent with the apparently wet climate of the region in that period. It seems that this TWSA-based conclusion is dominated by an overall downward trend in TWSA in the region. To overcome this limitation, using a signal decomposition approach, Hosseini-Moghari et al.56 proposed a modifed GRACE-based drought index called modifed total storage defcit index (MTSDI), designed to obtain drought information consistent with meteorological variability even when there is a signifcant trend in the data. Inspired by their approach, this paper introduces a modifed drought index based on Zhao et al.41 termed modifed total water storage defcit index (MTWSDI). Unlike the previously introduced MTSDI, MTWSDI can be used to diferentiate between meteorological droughts and anthropogenic droughts using GRACE observations. Additionally, this study defnes a threshold-based drought monitoring and recovery framework using GRACE observations, specifcally designed for drought management, early warning, and recovery assessment. We carried out drought monitoring over two irrigated basins in humid and arid climates (see Fig. 1). Te selected study areas are two basins with signifcant water consumptions. Te Ganges–Brahmaputra basin located in South Asia in humid climate with an area of 1,689,100 km 2 and 21.26% irrigated area 52. Tis basin is located within India (more than 60%), China, Nepal, Bangladesh and Bhutan. Ganges and Brahmaputra receive annually 1,550 and 2025 mm precipitation, respectively 58. Life of over half a billion people in rural areas within the basin relies on agriculture and availability of freshwater 59. Te second basin is the Markazi basin in Iran, located in arid climate with an area of 844,356 km 2 and annual precipitation of 165 mm. Te Markazi basin receives 29% of the total renewable water resources of Iran 60, while most of the population live in this area. In addition, in the rural area of the basin, agriculture is the regular job where most groundwater is used to meet the irrigation needs. Considering that the occupation of many people in two basins is linked to water, drought impacts are more intense in these areas.

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Residuals Trend Seasonal (annual+semi-annual) TWSA 300

200 m] 100 [m s 0 gnal si -100

-200 TWSA (a) -300 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 200 Time [months]

m] 100 [m

ls 0 gna si

-100 TWSA (b) -200 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Time [months]

Figure 2. TWSA time series and its components over (a) the Ganges–Brahmaputra basin and (b) the Markazi basin. TWSA time series decomposed into long-term trend (red lines), annual and semi-annual amplitudes (green lines) and residues (purple columns). Residues were calculated by subtracting the long-term trend and annual and semi-annual terms from TWSA.

Standardized index Category D-scale − 0.50 to − 0.79 Abnormally dry D0 − 0.80 to − 1.29 Moderate drought D1 − 1.30 to − 1.59 Severe drought D2 − 1.60 to − 1.99 Extreme drought D3 − 2.0 or less Exceptional drought D4

Table 1. Drought categories information based on standardized drought indices and their corresponding D-scale.

Results Assessing TWSA time series. Figure 2 shows TWSA time series and its components including the trend, seasonality, and residuals (see “Data and methods” sections). In time series of the Ganges–Brahmaputra basin, a decreasing trend exists (Fig. 2a, red line), which is signifcant at 95% confdence level based on the Mann–Ken- dall trend test. Tis trend indicates annual loss of − 15.48 mm of water storage over the basin. For the Markazi basin (Fig. 2b), in the frst 3 years, there exists an insignifcant increasing trend, and aferwards a signifcant decreasing trend with the loss rate of − 15.05 mm/year. A continuous negative trend in both time series is evidence of the anthropogenic efect on TWSA over the basins. Figure 2c,d illustrate residuals of TWSA time series of the Ganges–Brahmaputra and Markazi basins, respectively. In our study, we decompose TWSA to systematic (e.g., possible trends, annual variability and seasonal changes) and stochastic terms (here, residuals)—See “Data and methods” section. Residual is defned as the diference between the TWSA and the sum of other systematic terms. We then use normalized residuals to describe meteorological droughts in the selected basins. In Fig. 2, continuous negative values of residuals indicate drought events. We have developed the Total Water Storage Defcit Index (TWSDI) and Modifed Total Water Storage Defcit Index (MTWSDI) based on TWSA and its residuals time series, respectively. Furthermore, time series of the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) on diferent time scales have been calculated over two studied basins as a benchmark for meteorological variability. Table 1 illustrates the drought categorization

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Ganges–Brahmaputra Markazi Indices TWSDI MTWSDI TWSDI MTWSDI SPI 1 0.05 0.13 − 0.05 0.13 SPI 3 0.13 0.31 0.00 0.33 SPI 6 0.12 0.40 0.16 0.49 SPI 9 0.06 0.48 0.26 0.57 SPI 12 0.07 0.57 0.31 0.62 SPI 24 0.04 0.54 0.42 0.61 SPI 36 0.01 0.50 0.29 0.39 SPEI 1 0.05 0.17 0.10 0.11 SPEI 3 0.12 0.37 0.19 0.27 SPEI 6 0.11 0.49 0.27 0.39 SPEI 9 0.05 0.56 0.32 0.45 SPEI 12 0.06 0.61 0.35 0.51 SPEI 24 − 0.01 0.59 0.43 0.46 SPEI 36 − 0.02 0.50 0.25 0.24

Table 2. Correlation coefcients between GRACE based drought indices and metrological drought indices on a diferent time scale. Bold values indicate signifcance < 0.05 based on t-test.

based on the selected indices, ranging from abnormal dry (i.e., a standardized index value less than − 0.5) to exceptional drought (i.e., a standardized index value less than − 2).

Correlations between GRACE‑based drought indices and metrological drought indices. Table 2 shows the Correlation Coefcients (CCs) between GRACE-based drought indices and SPI/SPEI. Based on the results, in the Ganges–Brahmaputra basin, the commonly used TWSDI is poorly correlated with SPI and SPEI. CCs between TWSDI and SPI/SPEI are not statistically signifcant at any time scale (at 0.05 signifcance level). Tis is a major limitation of the existing methods. Meanwhile, CCs between our proposed index (i.e., MTWSDI) and SPI/SPEI on a 12-month scale reach 0.57 and 0.61, respectively. All CCs are signifcant on all the time scales (at 0.05 signifcance level) except the 1-month SPI. Note that droughts are ofen defned at longer than 1-month time scale to ensure persistence of dryness. For the Markazi basin, although TWSDI is weakly correlated with the metrological drought indices on long timescales, MTWSDI still outperformed TWSDI. Te highest CC value was obtained between MTWSDI and SPI/SPEI on a 12-month time scale, being equal to 0.62 and 0.51, respectively. Based on the results, GRACE-based drought indices may not be appropriate for detecting meteorological droughts at short-time scales (e.g., 1- to 3-month SPI and SPEI). However, MTWSDI can be efciently used for drought monitoring at longer time scales. Tis highlights that GRACE-based drought analysis ofen contradicts the meteorological condition primarily due to trends in the data. Te proposed method leads to an expected consistency between anthropogenic drought (based on GRACE) and meteorological droughts, yet allows dif- ferentiating between the two. Considering the fact that CCs between GRACE-based drought indices and 12-month SPI/SPEI were the largest, we have compared these time series in Fig. 3 for more detailed analysis. As shown in Fig. 3a, in the Ganges–Brahmaputra basin, although TWSDI during 2009–2010 has the same behaviour as the meteorological indicators (SPI and SPEI), it highly underestimates drought severity. Generally, TWSDI time series follow the trend in the original TWSA (here, a negative trend). MTWSDI, on the other hand, corresponds well with the SPI and SPEI behaviour. However, in some cases there is a lag between detected drought events. For example, the meteorological drought event between 2014 and 2015 was translated to a drought event in 2015–2016 based on MTWSDI. Tis showed the storage changes in the basin may occur afer some months. In the Markazi basin, TWSDI exhibits a decreasing trend that does not correspond to meteorological droughts, and is likely the result of anthropogenic impact such as extensive overexploitation of groundwater57, 61. Terefore, TWSDI alone may not be suitable for describing droughts based on both meteorological and anthropogenic perspectives, at least in places with a substantial downward or upward trend. However, investigating both TWSDI and MTWSDI ofers the opportunity to decompose the human and the climate signals. Figure 3 also shows drought defcits based on the widely used TWSDI (Defcits, D) and the proposed MTWSDI (Medfeld Defcits, MD). As shown, D time series indicate a cumulative increasing pattern showing the combined efect of climate and human activities. Meanwhile, MD time series almost coincides with droughts recognized by SPI12 and SPEI12 and allows assessing the fraction of defcit that corresponds to meteorological drought events (see Fig. 3).

Assessing drought using GRACE‑based drought indices. For further evaluations, we have assessed drought events detected by TWSDI and MTWSDI over both basins. A drought event was considered as a period in which the standardized drought index remained less than − 0.5 for at least four continuous months. Based on TWSDI, three drought events were detected over the Ganges–Brahmaputra basin during 2002–2017; all drought events occurred afer April 2009 (see Table 3). Te frst drought occurred from April 2009 to Aguste 2010 with a total severity of − 1,445 km3 months (see “Data and methods” section for more information), which coincided

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Figure 3. Time series of GRACE-based drought indices (TWSDI and MTWSDI), metrological drought indices (SPI and SPEI), defcit (D) and modifed defcit (MD) over (a) the Ganges–Brahmaputra basin and (b) the Markazi basin.

TWSDI MTWSDI Coincides with a Coincides with a Duration Total severity meteorological Duration Total severity meteorological Basin Time period (months) (km3 months) drought? Time period (months) (km3 months) drought? Apr-09 to Aug-10 17 − 1,445 Yes Jul-02 to May-03 11 − 495 Yes Apr-12 to Nov-13 20 − 1,437 No Jul-06 to Feb-07 8 − 345 Yes Ganges–Brahma- Apr-14 to Jul-15 16 − 1,670 Yes Apr-09 to Aug-10 17 − 1,315 Yes putra Aug-15 to Mar-17 20 − 4,198 No Oct-15 to Jun-16 9 − 494 Yes Sep-16 to Mar-17 19 − 282 Yes Apr-11 to Mar-17 72 − 3,726 No Mar-08 to Nov-09 21 − 341 Yes Markazi Jun-10 to Sep-12 28 − 291 Yes

Table 3. Detected drought events based on TWSDI and MTWSDI from 2002 to 2017 (at least four continuous months with TWSDI/MTWSDI less than − 0.5 were considered a drought event).

with a meteorological drought based on SPI and SPEI (see Fig. 3). Tis is an example in which both the meteorological and GRACE-based information are consistent. Te second drought event spans from April 2012 to November 2013 which is not consistent with SPI and SPEI results. Te last one is a long-term 26-month drought event from April 2014 till the end of the study period. Te latter event is consistent with the meteorological conditions between April 2014 and July 2015; however, it is not consistent with the meteorological conditions between August 2015 and March 2017. Tis suggests that the GRACE signal mainly shows the anthropogenic

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None (No drought) D0 (Abnormally Dry) D1 (Moderate Drought) D2 (Severe Drought) D3 (Extreme Drought) D4 (Exceptional Drought) TWSA Trend

0 0 -10 -10 ] ] 3 3 -5 -5 m -30 -30 m [k [k -10 -10 t t ci -50 -50 ci fi fi -15 -15 /year] /year] 3 3 de de

-70 -70 m -20 -20 m of [k of [k d d

-90 -90 d -25 -25 d ol ol en en sh -110 -110 sh Tr -30 -30 Tr re re -130 -130 -35 -35 Th Th -150 -150 -40 -40 (a) Jan. Feb. Mar. Apr. MayJun.Jul.Aug.Sep.Oct.Nov.Dec. (b) Jan. Feb. Mar. Apr. MayJun. Jul. Aug. Sep. Oct. Nov. Dec.

Figure 4. Tresholds of defcit related to each drought category in each month for (a) the Ganges–Brahmaputra basin and (b) the Markazi basin. Te TWSA trend of each month during 2002–2017 is plotted.

impacts on local water availability rather than meteorological drought in the river basin. Based on our proposed approach (i.e., MTWSDI), fve drought events were recognized over the Ganges–Brahmaputra basin during the study period. Te longest one spans from April 2009 to August 2010 with a total severity of − 1,315 km3 months. All drought events based on MTWSDI coincided with meteorological droughts. For the Markazi basin, only one drought event was detected by the commonly used TWSDI from April 2011 to end of the study period in which the basin lost about 57.75 km3 water on average each month (total severity of − 3,726 km3 months). However, this event is not consistent with meteorological information (Fig. 3). Meanwhile, MTWSDI identifes two drought events, both consistent with meteorological (SPI and SPEI) information (Fig. 3). Te most severe drought is a 21-month event from March 2008 to November 2009 with a total severity of − 341 km3 months (see Table 3). Te results show that the proposed MTWSDI performs better than the commonly used TWSDI in term of being consistent with meteorological condition. Tat is because TWSDI represents the combined impacts of both meteorological drought and anthropogenic activities on water availability, while MTWSDI represents impacts of meteorological drought on relatively short time scale. By comparing MTWSDI with TWSDI, the proposed method allows decomposing the impacts of meteorological drought and anthropogenic activities on water availability for more informed drought assessment. It should be noted that the de-trending in MTWSDI would inevitably remove impacts of meteorological drought at long time scale (36 months). Tus, the decomposed anthropogenic impacts may contain leaked signal from long term meteorological trend. However, the SPI data in the two basins show little long term trend, suggesting impact from long term meteorological trend is minor.

GRACE‑based thresholds for drought monitoring. As shown, in the previous sections, MTWSDI outperformed TWSDI for describing events consistent with meteorological conditions. Terefore, in the rest of this paper, we investigate the possibility to monitor meteorological drought solely based on MTWSDI. Figure 4 illustrates thresholds of defcit corresponding to each drought category and month (see “Data and methods” section). For instance, in January at the Ganges–Brahmaputra basin (Fig. 4a), an abnormally dry situation will start with a defcit of − 23.46 km3, a moderate drought results from a defcit between [− 37.57, − 61.05] km 3. A defcit ranging [− 61.05, − 75.14] km 3 indicates a severe drought, whereas a defcit of [− 75.14, − 93.93] km 3 corresponds to an extreme drought. An event would be considered an exceptional drought if the absolute value of defcit exceeds − 93.93 km3 in January. For the Markazi basin, due to its arid climate, the absolute value of drought threshold is less than the Ganges–Brahmaputra basin. For example, in January, an abnormally dry situation corresponds to a defcit between − 6.65 and − 10.64 km3. In addition, moderate, severe, extreme and exceptional drought defcit thresholds are − 10.64, − 17.29, − 21.28 and − 26.60 km3, respectively. Tese thresholds can be calculated for diferent regions around the world to develop a defcit-based drought monitoring system using GRACE observations. Te monthly trend of TWSA is also plotted in Fig. 4 (see the black lines). Te trend of TWSA is negative over all the months, indicating a consistent increase of water defcit in the two basins. At the Ganges–Brahmaputra basin in all months (except July and August), water loss per year as indicated by the trend of TWSA is equivalent to the abnormally dry condition. As little change in SPI or SPEI is found, the water defcit is mainly a consequence of human impacts. In other words, water defcit caused by human activities every year is equivalent to water defcit in an abnormally dry situation, posing an inevitable anthropogenic trend on water availability. In the Markazi basin, the monthly trend in TWSA time series shows a defcit every year equivalent to a moderate drought (except February and March which corresponds to an abnormally dry condition). Tus, the anthropogenic impacts at the Markazi basin can be considered as a perpetual anthropogenic drought with accelerated severity. In general, anthropogenic drought refers to conditions in which human activities cause or intensify droughts18. In this particular case, the results show that human impacts mainly through overexploitation of groundwater has caused anthropogenic drought even when there is no precipitation defcit from a meteorological viewpoint. For this reason, a constant and alarming decline in TWS can be seen over time with water loss every year approximately equivalent to the defcit from a moderate drought.

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Figure 5. Tresholds of minimum and average time to drought recovery related to each drought category in each month for (a,c) the Ganges–Brahmaputra basin and (b,d) the Markazi basin.

GRACE‑based thresholds for drought recovery. Having estimates about water defcit (Fig. 4), we computed minimum and average time (months) to recover from each drought category in each month (Fig. 5)— see “Data and methods” section for details. Figure 5 shows how long it takes to recover from diferent defcits. For example, over the Ganges–Brahmaputra basin in January, for a recovery from an abnormally dry situation to normal situation, minimum 1–1.5 months and on average between 3.2 and 5.15 months are needed. As another example, to recover an exceptional drought corresponding to MTWSDI equal to − 2, minimum 3.8 and on average 12.8 months are required. For the Markazi basin, in January, an abnormally dry situation disappears minimum afer 1.2–2 months and on average it will take between 5.2 and 8.3 months. In addition, for thresholds of moderate, severe, extreme and exceptional drought also minimum of 2, 3.2, 3.9 and 4.9 months and on average 8.3, 13.6, 16.7 and 20.8 months are required. Figure 5 shows the results for other months. It should be noted that absolute values of defcit thresholds and defcit changes in the Markazi basin are less than those in the Ganges–Brahmaputra basin (see “Data and methods” sections). Terefore, recovery times are not very diferent. Discussion GRACE observations provide useful information for hydrological studies 62. However, considering that its observations include imprints from both climatic variability and anthropogenic interventions, linking GRACE observations to meteorological variability or anthropogenic impact is not always straightforward. In this study, a novel approach was introduced for drought monitoring over basins with signifcant human water use using a signal decomposition technique. Te proposed approach, named Modifed Total Water Storage Defcit Index (MTWSDI), decomposes the original GRACE signal and uses the residuals of the signal instead for meteorological drought monitoring and assessment. Tis allows for separating any possible deterministic trend (e.g., due to unsustainable water use and long term meteorological trend) from relatively short term meteorological variability. We applied our approach for meteorological drought monitoring over the Ganges–Brahmaputra in India and Markazi basins in Iran. Our fndings showed that MTWSDI exhibits consistency with meteorological drought indices (e.g., SPI and SPEI) in both basins. Comparing the GRACE-based indices with meteorological drought indices, the results indicated that the proposed MTWSDI outperformed the commonly used TWSDI. Te new developed MTWSDI captured the meteorological droughts as indicated by the meteorological drought indices and reported in previous works51, 63–67. Te TWSDI, however, showed signifcant downward trends that is likely caused by human-induced water defcit such as groundwater overexploitation. In line with previous studies, the results illustrated that GRACE observations may not be suitable to study short-term meteorological drought, while they are efcient for long-term meteorological and anthropogenic drought monitoring 41, 49. We, for the frst time, have developed a threshold-based drought monitoring and recovery using GRACE observations, providing useful information about volume of defcits, and minimum and average time for drought recovery. Tis approach provides unique information for decision-makers for planning and management. Based on the developed thresholds, it is shown that water defcits caused by anthropogenic impacts every year as indicated by the trend of TWS of the Ganges–Brahmaputra basin and Markazi basins is almost equal to water defcit in an abnormally dry condition and a moderate drought, receptively. In other words, unsustainable human water use has led to a form of perpetual and accelerated anthropogenic drought. Continuation of this trend would deplete the basin and cause signifcant socio-economic challenges. As a fnal remark, the proposed approach may be applied in both heavy irrigated/managed basins as well as more natural systems.

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Data and methods Data availability. GRACE Release 06 Mascon Solutions 68 produced by the Center for Space Research (CSR, https://www2.csr.utexas.edu/grace/RL06_mascons.html, Last accessed: 21 August 2019) with a 0.25° × 0.25° spatial resolution, were used in this study. While the CSR RL06 is available at a 0.25° scale, the native resolution is much courses and hence, the GRACE data is recommended for application to large basins (e.g., with an area larger than 200,000 km269). Te reason for using mascon solutions is that many studies, such as Scanlon et al.70, revealed that mascon solutions have advantages relative to the spherical harmonics solutions including less leakage error. Also among available mascon solutions, CSR mascon solutions can be used for hydrological applications without applying any gain factors or any post-processing68, 71. We calculated an area-weighted monthly TWSA time series of GRACE from April 2002 to March 2017, and applied a linear interpolation for flling monthly gaps in data with the same assumptions proposed by Solander et al.38. We assumed that the length of GRACE observations could be considered a representation of the long-term behaviour of TWSA over the basins. We acknowledge the limitations of this assumption. However, this is currently the longest satellite-based TWSA time series available. Also, for precipitation and potential evapotranspiration (PET), Climatic Research Unit (CRU) time series dataset 72 version 4.03 (https://crudata.uea.ac.uk/cru/data/hrg/, Last accessed: 21 August 2019) were used between 1980 and 2018, respectively. Precipitation and potential evapotranspiration data had monthly temporal resolution and 0.5° × 0.5° spatial resolution. Te root mean square (RSM) values of TWSA (plotted in Fig. 1) is calculated using Eq. (1) as follows:

T 1 2 RMS = TWSAt (1) T t=1 where TWSAt is TWSA in period t and T is the length of TWSA time series.

Drought indices. TWSDI used by Zhao et al.41 is a standardization of TWSA time series month-to-month as follows:

TWSAi·j − TWSAj TWSDIi·j = (2) SDTWSAj

where TWSDIi·j is Total Water Storage Defcit Index for ith year and jth month, TWSAi·j is total water storage anomaly in ith year and jth month (mm), TWSAj is average of TWSA for jth month (mm), and SDTWSAj is the standard deviation of TWSA for jth month (mm). As mentioned before, this TWSA in heavy irrigated basins is signifcantly afected by human water use, among other climatic factors. To address this issue, we used a signal decomposition technique. Te GRACE signal consisted of four terms as follows 70: TWSA = Trend + Annual signal + Semi−annual signal + Residuals (3) where TWSA is total signal (mm), Trend is long-term behaviour of TWSA time series (mm), Annual and Semi-annual signals are annual and semiannual (i.e., representing seasonality) amplitudes of TWSA (mm), and Residuals (mm) are the diference between the TWSA and the sum of other three terms mentioned earlier. Except Residues, all other terms exhibit a deterministic behaviour and hence can be removed. TWSA trend can be removed using a linear trend line and Annual and Semi-annual signals can be extracted by ftting sines and/ or cosines functions 70. As a result, we can modify TWSDI as follows:

Residualsi·j − Residualsj MTWSDIi·j = (4) SDResidualsj

where MTWSDIi·j is Modifed Total Water Storage Defcit Index for ith year and jth month, Residualsi·j is remain- ing of total signal for ith year and jth month (mm), Residualsj is average of Residuals related to jth month (mm), and SDResidualsj is the standard deviation of Residuals for jth month. Additionally, SPI and SPEI time series were calculated as a benchmark to evaluate GRACE-based drought indices. SPI and SPEI calculations were performed following recommendations of McKee et al.73 and Vicente- Serrano et al.74, respectively and considering suggestions of Stagge el al.75. SPI and SPEI were computed using precipitation and PET from CRU TS data. Table 1 presents categorization of the drought based on all indices used in this study.

Defcit. We considered Eqs. (5) and (6) as the storage defcit: −6 Di.j = TWSAi·j − TWSAj × Area × 10 (5) −6 MDi·j = Residualsi·j − Residualsj × Area × 10 (6)

3 where Di.j and MDi·j are defcit and modifed defcit (km ) for ith year and jth month, respectively, and Area is the area of basin (km 2).

Drought severity. Drought Severity was considered as summation of defcits amounts during a given drought event. We computed the total severity during the drought event as follows:

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= × Severity n Deﬁcit n Durationn (7)

3 where Severity is total severity within nth drought event (km months), Deﬁcit n is average of storage defcit (either D or MD) during nth drought event (km 3) and Duration is number of months of nth event drought (months).

Drought threshold. Tis study used the month-to-month drought index and defcit time series to defne a defcit threshold for each drought category. We ftted liner equations between drought indices and their corresponding defcit for each month. Ten, these equations were used to determine the defcit threshold based on upper bound of drought indices in each category (see Table 1) as follows:

Thresholdj·d = aj × UBd + bj (8)

where Thresholdj·d is the defcit threshold in jth month for dth drought category, UBd is upper bound of drought index in dth drought category, and aj and bj are the regression coefcient and intercept for jth month, respectively. Considering Eq. (4) and Eq. (6), bj would be equal to zero.

Threshold of drought recovery. Based on time series of MD (or D), we can compute time series of defcit changes based on central diference approaches as: d DM(t + �t) − DM(t − �t) DM(t) = (9) dt 2�t d where dt DM(t) is defcit change in tth time step, and for the monthly evolution t is equal to one month. Tis study used the approach introduced by Tomas et al.42 to determine drought recovery based on ftting empirical d (Kaplan–Meier) cumulative distribution (eCDF) on dt DM(t) time series. Te values corresponding to probability of 0.95 and 0.68, based on ftted eCDF, represent the maximum and average positive change of defcits42. Finally, we divided drought defcit thresholds by maximum and average rate of defcit changes to calculate thresholds of minimum and average time to recovery, respectively.

Separating monitoring metrological drought from anthropogenic impact. We have used the residuals and trend of TWSA to quantity metrological drought and anthropogenic impact, respectively. We acknowledge that this is a proxy approach and it is subject to uncertainty, especially in areas where there is a signifcant evidence of change in meteorological droughts. To justify our assumption, we have analyzed the time series of precipitation and potential evapotranspiration as the main variables that control water availability and meteorological droughts in a natural basin. During the period of GRACE observations (2002–2017), at both study areas, the datasets do not exhibit any statistically signifcant trend based on a Mann Kendall test at the 0.01 signifcance level (99% confdence level). Terefore, it is reasonable to assume that the decreasing trend in TWSA is related to human activity not meteorological changes. In such a situation, the possible trends in time series should be removed for monitoring meteorological drought. Further, removing the seasonality (climatol- ogy) is a commonly used in drought indices not only GRACE-based drought indices but also other standardized drought indices. Afer removing the trend and seasonality, the residuals remain that exhibit random patterns consistent with meteorological variability (but signifcantly diferent than permanent signals such as increased human water use over time). Terefore, de-trended and de-seasonalized GRACE TWSA can be used as proxy to extract the climatic drought signals 76. In the next step, we calculated the volume of water that the two basins lost every year due to the downward trend. Ten, based on the volume of water lost in each month due to human water use (based on the trend data) relative to the threshold of defcit for each drought category in each month (see Fig. 4), we separated meteorological droughts and anthropogenic impacts. It should be noted that another approach for separating meteorological droughts from anthropogenic droughts is using ancillary data like outputs of land surface models (LSMs) or global hydrological models (GHMs). Tis approach would be preferred upon availability of reliable and calibrated model simulations. However, most of the existing global models and even local land-surface models are uncalibrated77 and many of them do not simulate groundwater at all 78. Tere- fore, using their outputs for separating meteorological and anthropogenic factors are subject to large uncertainty.

Received: 12 November 2019; Accepted: 17 May 2020

References 1. Bachmair, S. et al. Drought indicators revisited: Te need for a wider consideration of environment and society. Wiley Interdiscip. Rev. Water 3, 516–536 (2016). 2. Hosseini-Moghari, S. M. & Araghinejad, S. Monthly and seasonal drought forecasting using statistical neural networks. Environ. Earth Sci. 74, 397–412 (2015). 3. Haile, G. G. et al. Droughts in East Africa: Causes, impacts and resilience. Earth-Sci. Rev. 193, 146–161 (2019). 4. Madadgar, S., AghaKouchak, A., Farahmand, A. & Davis, S. J. Probabilistic estimates of drought impacts on agricultural production. Geophys. Res. Lett. 44, 7799–7807 (2017). 5. Lesk, C., Rowhani, P. & Ramankutty, N. Infuence of extreme weather disasters on global crop production. Nature 529, 84. https ://doi.org/10.1038/nature16467 (2016). 6. Howitt, R., Medellín-Azuara, J., MacEwan, D., Lund, J. R. & Sumner, D. Economic analysis of the 2014 drought for California agriculture (Center for Watershed Sciences University of California, Davis, 2014). 7. Medellín-Azuara, J. et al. Economic analysis of the 2016 California drought on agriculture (Calif. Dept. Food and Ag. UC Davis Cent. for Watershed Sci., Davis, 2016).

Scientific Reports | (2020) 10:15098 | https://doi.org/10.1038/s41598-020-71837-7 9 Vol.:(0123456789) www.nature.com/scientificreports/

8. Borlina, C. S. & Rennó, N. O. Te impact of a severe drought on dust lifing in California’s Owens Lake Area. Sci. Rep. 7(1784), 17. https://doi.org/10.1038/s41598-41017-01829-41597 (2017). 9. Turco, M. et al. On the key role of droughts in the dynamics of summer fres in Mediterranean Europe. Sci. Rep. 7, 81. https://doi. org/10.1038/s41598-41017-00116-41599 (2017). 10. Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought–fre interactions. Proc. Natl. Acad. Sci. USA 111, 6347–6352 (2014). 11. Dean, J. G. & Stain, H. J. Mental health impact for adolescents living with prolonged drought. Aust. J. Rural. Health 18, 32–37 (2010). 12. Sartore, G. M. et al. Improving mental health capacity in rural communities: Mental health frst aid delivery in drought-afected rural New South Wales. Aust. J. Rural. Health 16, 313–318 (2008). 13. Bond, N. R., Lake, P. & Arthington, A. H. Te impacts of drought on freshwater ecosystems: an Australian perspective. Hydrobio- logia 600, 3–16 (2008). 14. MacFarquhar, N. Drought Chokes Of Iran’s Water and Its Economy Vol. 18 (New York Times, New York, 2001). 15. Sharma, S. & Mujumdar, P. Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India. Sci. Rep. 7, 15582. https://doi.org/10.11038/s41598-15017-15896-15583 (2017). 16. Mazdiyasni, O. & AghaKouchak, A. Substantial increase in concurrent droughts and heatwaves in the United States. Proc. Natl. Acad. Sci. USA 112, 11484–11489 (2015). 17. Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52 (2013). 18. Carnicer, J. et al. Widespread crown condition decline, food web disruption, and amplifed tree mortality with increased climate change-type drought. Proc. Natl. Acad. Sci. USA 108, 1474–1478 (2011). 19. AghaKouchak, A., Feldman, D., Hoerling, M., Huxman, T. & Lund, J. Recognize anthropogenic drought. Nature 524, 409 (2015). 20. Zhao, S., Cong, D., He, K., Yang, H. & Qin, Z. Spatial-temporal variation of drought in China from 1982 to 2010 based on a modifed Temperature Vegetation Drought Index (mTVDI). Sci. Rep. 7, 17473. https://doi.org/10.11038/s41598-17017-17810-17473 (2017). 21. Liu, L. et al. Te Microwave Temperature Vegetation Drought Index (MTVDI) based on AMSR-E brightness temperatures for long-term drought assessment across China (2003–2010). Remote Sens. Environ. 199, 302–320 (2017). 22. Tadesse, T. et al. Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: First results. Gisci. Remote. Sens. 54, 230–257 (2017). 23. Tan, M. L., Chua, V. P., Tan, K. C. & Brindha, K. Evaluation of TMPA 3B43 and NCEP-CFSR precipitation products in drought monitoring over Singapore. Int. J. Remote Sens. 39, 2089–2104 (2018). 24. Jiang, S. et al. Drought monitoring and reliability evaluation of the latest TMPA precipitation data in the Weihe River Basin, Northwest China. J. Arid Land 9, 256–269 (2017). 25. Agutu, N. et al. Assessing multi-satellite remote sensing, reanalysis, and land surface models’ products in characterizing agricultural drought in East Africa. Remote Sens. Environ. 194, 287–302 (2017). 26. Nicolai-Shaw, N., Zscheischler, J., Hirschi, M., Gudmundsson, L. & Seneviratne, S. I. A drought event composite analysis using satellite remote-sensing based soil moisture. Remote Sens. Environ. 203, 216–225 (2017). 27. Halwatura, D., McIntyre, N., Lechner, A. M. & Arnold, S. Capability of meteorological drought indices for detecting soil moisture droughts. J. Hydrol. Reg. Stud. 12, 396–412 (2017). 28. Zhang, X., Tang, Q., Liu, X., Leng, G. & Li, Z. Soil moisture drought monitoring and forecasting using satellite and climate model data over Southwestern China. J. Hydrometeorol. 18, 5–23 (2017). 29. Hao, Z., AghaKouchak, A., Nakhjiri, N. & Farahmand, A. Global integrated drought monitoring and prediction system. Sci. Data 1, 140001 (2014). 30. Tang, Q. et al. Hydrological monitoring and seasonal forecasting: Progress and perspectives. J. Geogr. Sci. 26, 904–920 (2016). 31. AghaKouchak, A. et al. Remote sensing of drought: Progress, challenges and opportunities. Rev. Geophys. 53, 452–480 (2015). 32. Syed, T. H., Famiglietti, J. S., Rodell, M., Chen, J. & Wilson, C. R. Analysis of terrestrial water storage changes from GRACE and GLDAS. Water Resour. Res. 44 (2008). 33. Ahmed, M. & Wiese, D. N. Short-term trends in Africa’s freshwater resources: Rates and drivers. Sci. Total Environ. 695, 133843 (2019). 34. Ahmed, M. et al. Integration of GRACE (Gravity Recovery and Climate Experiment) data with traditional data sets for a better understanding of the time-dependent water partitioning in African watersheds. Geology 39, 479–482 (2011). 35. Eicker, A., Schumacher, M., Kusche, J., Döll, P. & Schmied, H. M. Calibration/data assimilation approach for integrating GRACE data into the WaterGAP Global Hydrology Model (WGHM) using an ensemble Kalman flter: First results. Surv. Geophys. 35, 1285–1309 (2014). 36. Swenson, S. & Lawrence, D. A GRACE-based assessment of interannual groundwater dynamics in the Community Land Model. Water Resour. Res. 51, 8817–8833 (2015). 37. Freedman, F. R., Pitts, K. L. & Bridger, A. F. Evaluation of CMIP climate model hydrological output for the Mississippi River Basin using GRACE satellite observations. J. Hydrol. 519, 3566–3577 (2014). 38. Solander, K. C., Reager, J. T., Wada, Y., Famiglietti, J. S. & Middleton, R. S. GRACE satellite observations reveal the severity of recent water over-consumption in the United States. Sci. Rep. 7, 8723 (2017). 39. Ahmed, M., Sultan, M., Elbayoumi, T. & Tissot, P. Forecasting GRACE Data over the African watersheds using artifcial neural networks. Remote Sens. 11, 1769 (2019). 40. Yi, H. & Wen, L. Satellite gravity measurement monitoring terrestrial water storage change and drought in the continental United States. Sci. Rep. 6, 19909. https://doi.org/10.11038/srep19909 (2016). 41. Zhao, M., Velicogna, I. & Kimball, J. S. A global gridded dataset of grace drought severity index for 2002–2014: Comparison with PDSI and SPEI and a case study of the Australia millennium drought. J. Hydrometeorol. 18, 2117–2129 (2017). 42. Tomas, A. C., Reager, J. T., Famiglietti, J. S. & Rodell, M. A GRACE-based water storage defcit approach for hydrological drought characterization. Geophys. Res. Lett. 41, 1537–1545 (2014). 43. Zhao, M., Velicogna, I. & Kimball, J. S. Satellite observations of regional drought severity in the continental United States using GRACE-based terrestrial water storage changes. J. Clim. 30, 6297–6308 (2017). 44. Yirdaw, S. Z., Snelgrove, K. R. & Agboma, C. O. GRACE satellite observations of terrestrial moisture changes for drought characterization in the Canadian Prairie. J. Hydrol. 356, 84–92 (2008). 45. Awange, J., Schumacher, M., Forootan, E. & Heck, B. Exploring hydro-meteorological drought patterns over the Greater Horn of Africa (1979–2014) using remote sensing and reanalysis products. Adv. Water Resour. 94, 45–59 (2016). 46. Houborg, R., Rodell, M., Li, B., Reichle, R. & Zaitchik, B. F. Drought indicators based on model‐assimilated Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage observations. Water Resour. Res. 48 (2012). 47. Ma, S., Wu, Q., Wang, J. & Zhang, S. Temporal evolution of regional drought detected from GRACE TWSA and CCI SM in Yunnan Province, China. Remote Sens. 9, 1124 (2017). 48. Sinha, D., Syed, T. H., Famiglietti, J. S., Reager, J. T. & Tomas, R. C. Characterizing drought in India using GRACE observations of terrestrial water storage defcit. J. Hydrometeorol. 18, 381–396 (2017). 49. Cao, Y., Nan, Z. & Cheng, G. GRACE gravity satellite observations of terrestrial water storage changes for drought characterization in the arid land of northwestern China. Remote Sens. 7, 1021–1047 (2015).

Scientific Reports | (2020) 10:15098 | https://doi.org/10.1038/s41598-020-71837-7 10 Vol:.(1234567890) www.nature.com/scientificreports/

50. Pan, Y. et al. Detection of human-induced evapotranspiration using GRACE satellite observations in the Haihe River basin of China. Geophys. Res. Lett. 44, 190–199 (2017). 51. Humphrey, V., Gudmundsson, L. & Seneviratne, S. I. Assessing global water storage variability from GRACE: Trends, seasonal cycle, subseasonal anomalies and extremes. Surv. Geophys. 37, 357–395 (2016). 52. Scanlon, B. R. et al. Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data. Proc. Natl. Acad. Sci. USA. 201704665 (2018). 53. Ahmed, M., Sultan, M., Wahr, J. & Yan, E. Te use of GRACE data to monitor natural and anthropogenic induced variations in water availability across Africa. Earth-Sci. Rev. 136, 289–300 (2014). 54. Tang, Q. et al. Dynamics of terrestrial water storage change from satellite and surface observations and modeling. J. Hydrometeorol. 11, 156–170 (2010). 55. Tang, Q., Zhang, X. & Tang, Y. Anthropogenic impacts on mass change in North China. Geophys. Res. Lett. 40, 3924–3928 (2013). 56. Hosseini-Moghari, S. M., Araghinejad, S., Ebrahimi, K. & Tourian, M. J. Introducing modifed total storage defcit index (MTSDI) for drought monitoring using GRACE observations. Ecol. Indic. 101, 465–475 (2019). 57. Chao, N., Luo, Z., Wang, Z. & Jin, T. Retrieving groundwater depletion and drought in the Tigris-Euphrates Basin between 2003 and 2015. Groundwater 56, 770–782 (2018). 58. Mirza, M. M. Q. Climate change, fooding in South Asia and implications. Reg. Environ. Change 11, 95–107 (2011). 59. Pervez, M. S. & Henebry, G. M. Projections of the Ganges–Brahmaputra precipitation—Downscaled from GCM predictors. J. Hydrol. 517, 120–134 (2014). 60. Frenken, K. Irrigation in the Middle East region in fgures AQUASTAT Survey-2008. Water Reports. https://www.fao.org/docre p/012/i0936e/i0936e00.htm (2009). 61. Rajabi, A. M. A numerical study on land subsidence due to extensive overexploitation of groundwater in Aliabad plain, Qom-Iran. Nat. Hazards 93, 1085–1103 (2018). 62. Moiwo, J. P., Yang, Y., Li, H., Han, S. & Hu, Y. Comparison of GRACE with in situ hydrological measurement data shows storage depletion in Hai River basin, Northern China. Water Sa 35, 663–670 (2009). 63. Papa, F. et al. Satellite-derived surface and sub-surface water storage in the Ganges–Brahmaputra River Basin. J. Hydrol. Reg. Stud. 4, 15–35 (2015). 64. Chen, J., Li, J., Zhang, Z. & Ni, S. Long-term groundwater variations in Northwest India from satellite gravity measurements. Glob. Planet. Change 116, 130–138 (2014). 65. Shean, M. Iran: 2008/2009 wheat production declines due to drought. United States Department of Agriculture, Foreign Agricultural Service. https://ipad.fas.usda.gov/highlights/2008/05/Iran_may2008.htm (2008). 66. Dezfuli, A. K., Karamouz, M. & Araghinejad, S. On the relationship of regional meteorological drought with SOI and NAO over southwest Iran. Teor. Appl. Climatol. 100, 57–66 (2010). 67. Nair, C. Tackling a drought in Iran. New Your Times November. https://www.nytimes.com/2014/11/10/opinion/tackling-a-droug ht-in-iran.html (2014). 68. Save, H., Bettadpur, S. & Tapley, B. D. High-resolution CSR GRACE RL05 mascons. J. Geophys. Res-Earth 121, 7547–7569 (2016). 69. Reager, J. & Famiglietti, J. S. Characteristic mega-basin water storage behavior using GRACE. Water Resour. Res. 49, 3314–3329 (2013). 70. Scanlon, B. R. et al. Global evaluation of new GRACE mascon products for hydrologic applications. Water Resour. Res. 52, 9412–9429 (2016). 71. Wiese, D. N., Landerer, F. W. & Watkins, M. M. Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solu- tion. Water Resour. Res. 52, 7490–7502 (2016). 72. Harris, I., Jones, P., Osborn, T. & Lister, D. Updated high-resolution grids of monthly climatic observations—Te CRU TS3. 10 Dataset. Int. J. Climatol. 34, 623–642 (2014). 73. McKee, T. B., Doesken, N. J. & Kleist, J. Te relationship of drought frequency and duration to time scales. Am. Meteorol. Society Boston MA 17, 179–183 (1993). 74. Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: Te standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010). 75. Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F. & Stahl, K. Candidate distributions for climatological drought indices (SPI and SPEI). Int. J. Climatol. 35, 4027–4040 (2015). 76. Chao, N., Wang, Z., Jiang, W. & Chao, D. A quantitative approach for hydrological drought characterization in southwestern China using GRACE. Hydrogeol. J. 24, 893–903 (2016). 77. Döll, P., Douville, H., Güntner, A., Schmied, H. M. & Wada, Y. Modelling freshwater resources at the global scale: Challenges and prospects. Surv. Geophys. 37, 195–221 (2016). 78. Zeng, Y. et al. Global land surface modeling including lateral groundwater fow. J. Adv. Model. Earth. Syst. 10, 1882–1900 (2018). Acknowledgements Te authors are particularly grateful to the Center for Space Research (CSR), University of Texas, Austin and the Climatic Research Uni, University of East Anglia for providing GRACE and CRU products, respectively. Tis research is partially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA20060402), the National Natural Science Foundation of China (41790424, 41730645 and 41425002), and CAS-VPST Silk Road Science Fund 2018 (131A11KYSB20170113). Author contributions S.M. H.M led the idea, analysis, and writing. S.A., K.E., Q.T. and A.A. contributed to editing of the manuscript. A.A. and Q.T. contributed to analysis of the results and the discussion. All authors reviewed the manuscript.

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